(singke) #1


Sequencing metabolically labeled transcripts in

single cells reveals mRNA turnover strategies

Nico Battich, Joep Beumer, Buys de Barbanson, Lenno Krenning†, Chloé S. Baron‡,
Marvin E. Tanenbaum, Hans Clevers, Alexander van Oudenaarden

The regulation of messenger RNA levels in mammalian cells can be achieved by the modulation of
synthesis and degradation rates. Metabolic RNA-labeling experiments in bulk have quantified these rates
using relatively homogeneous cell populations. However, to determine these rates during complex
dynamical processes, for instance during cellular differentiation, single-cell resolution is required.
Therefore, we developed a method that simultaneously quantifies metabolically labeled and preexisting
unlabeled transcripts in thousands of individual cells. We determined synthesis and degradation rates
during the cell cycle and during differentiation of intestinal stem cells, revealing major regulatory
strategies. These strategies have distinct consequences for controlling the dynamic range and
precision of gene expression. These findings advance our understanding of how individual cells in
heterogeneous populations shape their gene expression dynamics.


ammalian cells use diverse strategies
to regulate mRNA levels by controlling
their synthesis and degradation rates
( 1 , 2 ). High synthesis or degradation
rates allow cells to rapidly respond to
extracellular and intracellular signals ( 2 , 3 ),
whereas low degradation rates allow them
to integrate transcriptional information over
time ( 4 ). The extent to which mammalian cells
exploit different regulatory strategies during
complex dynamical processes such as cell cycle
progression or organ formation remains un-
clear. This is partially due to the difficulty in
distinguishing these strategies when only tran-
script levels are measured.
Additionally, the study of these regulatory
strategies in bulk assays is hindered by the
presence of heterogeneous cell types in the
same tissue and unsynchronized cell states
that result from the cell and circadian cycles
( 1 , 5 – 7 ). Advances in single-cell RNA sequenc-
ing help to resolve cellular heterogeneity
( 8 – 14 ), yet do not provide insights into how
the dynamic control of transcription and deg-
radation leads to the observed expression pat-
terns. The kinetic parameters that govern the
life of mRNA can be measured by its metabolic
labeling during transcription ( 1 , 5 , 6 ). Here, we
demonstrate that mRNA labeled with 5-ethynyl-
uridine (EU) can be detected in thousands of
single cells by sequencing. We determined
transcription and degradation rates in heter-
ogeneous and unsynchronized cell popula-

tions and uncovered mRNA control strategies
during the cell cycle of human cells and differ-
entiation of mouse intestinal stem cells.
To measure newly synthesized transcripts
in single cells, we labeled mRNA by incubat-
can be biotinylated with click chemistry, a
method we have named“single-cell EU-labeled
RNA sequencing”(scEU-seq) ( 15 ). Briefly, after
EU incubation, cells were dissociated, fixed,
and permeabilized, and EU-labeled RNA was
biotinylated in situ. We sorted single cells and
generated mRNA/cDNA hybrids using poly-T
primers containing a cell barcode, a unique
molecular identifier (UMI), a 5′sequencing
adapter, and the T7 promoter. Cells were pooled,
EU labeled and unlabeled hybrids were sep-
arated using streptavidin magnetic beads, and
libraries were generated for both fractions
(Fig. 1A). The UMI counts for labeled mRNA
were higher in EU-treated cells compared
with dimethyl sulfoxide (DMSO)–treated cells
or empty control wells, resulting in a high
signal-to-noise ratio and low across-well cross-
contamination rates (Fig. 1B). Only 12 of 11,848
detected genes were affected by the EU treat-
ment itself (fig. S1A). When we compared the
total mRNA (unlabeled and EU-labeled UMIs;
total UMIs) from EU-treated versus DMSO-
treated RPE1-FUCCI cells, we found high re-
covery efficiency (99.5 ± 0.4%) of labeled mRNA
(fig. S1B). After 120 min of EU incubation, the
labeled mRNA fraction was on average 8.9 ±
0.7% (fig. S1B), which agrees with an expected
average production of 8 to 10% of the tran-
scriptome during a period of 2 hours in un-
synchronized cells with a cell cycle length of
~20 to 24 hours ( 16 ).
To assess whether scEU-seq specifically en-
riches transcripts synthesized during the EU-
labeling window, we performed pulse and chase
experiments varying either the EU incubation
time or the length of a chase phase with uridine

(U) after EU treatment for 22 hours (Fig. 1C and
fig. S1, C to G). As expected, we detected an in-
crease in labeled UMIs as a function of the EU
pulse length (Fig. 1D) and a decrease as a func-
tion of the U chase length (Fig. 1E). We could
still detect significantly higher UMI counts for
pared with the DMSO control (Fig. 1D and fig.
S1G). In these short EU pulses, we found that
labeled UMIs were enriched in transcripts that
contained unspliced introns (Fig. 1F).
Next, we incubated K562 cells at either 37°C
or 42°C for a period of 45 min in the presence
of EU or DMSO. The differential gene expres-
sion signature upon heat shock was more
pronounced in the fraction of EU-labeled
mRNAs compared with the unlabeled frac-
tion or with cells treated with DMSO (fig. S2).
Consistently, the functional annotation anal-
ysis ( 17 ) for up-regulated genes in the EU-
labeled fraction revealed an enrichment for
genes encoding heat shock or stress response
proteins (fig. S2B). In addition, UMIs of these
stress response genes represented a large per-
centage in the EU-labeled samples but not in
the DMSO-treated or unlabeled controls.
Using data from the scEU-seq pulse and
chase experiments, we can estimate the synthe-
sis ratekand the degradation rate constantg
for all detected transcripts. Furthermore, we
can place individual cells along cell cycle or
differentiation trajectories and thus infer how
synthesis and degradation rates change over
time. We first estimatedkandgwith high res-
olution along the mammalian cell cycle. For
each of the 5422 cells that passed quality con-
trols in the pulse and chase experiments, we
calculated the relative position along the cell
cycle using the Geminin-GFP and the Cdt1-
RFP signals from the FUCCI system (fig. S3, A
and B) ( 18 ). The expression of known cell cycle
markers followed the expected pattern relative
to the Geminin-GFP and Cdt1-RFP ( 18 ), whereas
the housekeeping geneHPRT1displayed con-
stant expression during the cell cycle (Fig. 2A
and fig. S3C). The level of labeled transcripts
of cell cycle–controlled genes changed as a
function of the cell cycle (fig. S4 and fig. S5),
with the total UMI counts per cell approxi-
mately doubling during one cell cycle (fig. S6,
A and B). These results suggest that our es-
timation of the cell cycle progression in single
cells is accurate.
To fitkandgto the experimental datasets,
we simulated the dynamics of the pulse and
chase experiments and quantified the accu-
racy of the fitting procedures. The simulations
defined the range ofkandgvalues for which
we can accurately determine these rates, and
demonstrated that a model that does not as-
sume steady-state dynamics of gene expres-
sion is more fitting for our datasets (fig. S7).
Next, we used the cell cycle progression es-
timates to pool cells from different EU-labeling


Battichet al.,Science 367 , 1151–1156 (2020) 6 March 2020 1of5

Oncode Institute, Hubrecht Institute-KNAW (Royal
Netherlands Academy of Arts and Sciences) and University
Medical Center Utrecht, 3584 CT Utrecht, Netherlands.
*Corresponding author. Email: [email protected] (N.B.);
[email protected] (A.v.O.)
†Present address: Division of Cell Biology, Oncode Institute,
Netherlands Cancer Institute, Amsterdam, the Netherlands.
‡Present address: Stem Cell Program and Division of Hematology/
Oncology, Boston Children’s Hospital and Dana-Farber Cancer Institute,
Howard Hughes Medical Institute, Harvard Medical School, Harvard
Stem Cell Institute, Stem Cell and Regenerative Biology Department,
Harvard University, 1 Blackfan Circle, Boston, 02115 MA, USA.

Free download pdf